Measurement of Energy Expenditure During Laboratory and Field Activities

This dissertation was designed to examine the validity of heart rate (HR) and motion sensors for estimating energy expenditure (EE) during activities ranging from sedentary behaviors to vigorous exercise. A secondary purpose was to devise new ways to improve on current methods of estimating EE. Specific aims of the dissertation were: (1) to examine the use of pedometers to measure steps taken, distance traveled, and EE during treadmill walking at various speeds; (2) Examine the use of a Polar HR monitor to estimate EE during treadmill running, stationary cycling, and rowing; (3) compare the current Actigraph regression equations (relating counts·min-1 to EE) against three newer devices (Actiheart, Actical, and AMP-331) during sedentary, light, moderate, and vigorous intensity activities; and (4) development of a new 2-regression model to estimate EE using the Actigraph accelerometer. For the first aim, 10 participants performed treadmill walking for five minutes at five speeds while wearing two pedometers of different brands (10 pedometer brands were tested) on the right and left hip. Simultaneously oxygen consumption (VO2) was measured and actual steps were counted using a hand tally counter. Six of the 10 pedometers were within ± 3% of actual steps at 80 m·min-1 and faster. Most pedometers were within ± 10% of actual distance at 80 m·min-1, but they overestimate distance at slower speeds, and underestimate distance at faster speeds. Most pedometers gave estimates of gross EE within ± 30% of measured EE across all speeds. In general, pedometers are most accurate for assessing steps, less accurate for assessing distance, and even less accurate for assessing kcals. In the second aim, 10 males and 10 females performed a maximal treadmill test. On a separate day they performed treadmill, cycle, and rowing exercise for 10 minutes at three different intensities. During each trial EE was estimated using two Polar S410 HR monitors (one with predicted VO2max and HRmax (PHRM) and one with actual VO2max and HRmax (AHRM), input into the watch). Simultaneously, EE was measured by indirect calorimetry (IC). For males there were no differences among the mean values of EE for the AHRM, PHRM and IC for any exercise mode (P ≥ 0.05). In females, the AHRM significantly improved the estimate of EE compared to the PHRM (P \u3c 0.05), but it still overestimated mean EE on the treadmill and cycle (P \u3c 0.05). The Polar S410 HR monitor provides the best estimate of EE when the actual VO2max and HRmax are used. For the third aim, 48 participants performed various activities ranging from sedentary pursuits to vigorous exercise. The activities were split into three routines of six activities and each participant performed one routine. During each routine an Actigraph (right hip), Actical (left hip), Actiheart (chest), and AMP-331 (right ankle) were worn. Simultaneously, EE was measured by IC. The Actiheart HR algorithm was not significantly different from measured EE for any of the 18 activities (P ≥ 0.05). The Actiheart combined HR and activity algorithm was only significantly different from measured EE for vacuuming and ascending/descending stairs (P \u3c 0.05). All remaining prediction equations, for the devices examined, over- or underestimated EE for at least seven activities. The Actiheart HR algorithm provided the best estimate of EE over a wide range of activities. The Actical and Actigraph tended to overestimate walking and sedentary activities and underestimate most other activities. For the fourth aim, 48 participants performed various activities (sedentary, light, moderate, and vigorous intensities) that were split into three routines of six activities. Each participant performed one routine. During each test the participants wore an Actigraph accelerometer and EE was measured by IC. Forty-five tests were randomly selected for the development of the new equation, and 15 tests were used to cross-validate the new equation and compare against existing equations. For each activity the coefficient of variation (CV) of the counts per 10 seconds was calculated to determine if the activity was walking/running, or some other activity. If the CV ≤ 10 then a walking/running regression equation (relating counts·min-1 to METs) was used, while if the CV \u3e 10 a lifestyle/leisure time physical activity (LTPA) regression was used. The new 2-regression model explained 73% of the variance in EE for walking/running, and 83.8% of the variance in EE for lifestyle/LTPA and it was within ± 0.84 METs of measured METs for each of the 17 activities performed (P ≥ 0.05). The new 2-regression model is a more accurate prediction of EE then the currently published regression equations using the Actigraph accelerometer

Effect of ActiGraph\u27s Low Frequency Extension for Estimating Steps and Physical Activity Intensity

This study examined the effects of the ActiGraph’s (AG) low-frequency extension (LFE) filter on steps and physical activity classification in the free-living environment. Thirty-four African-American women (age, 24.5±5.2 years; BMI, 24.9±4.5 kg/m2) had daily activity measured simultaneously with an AG-GT3X+ accelerometer and a New Lifestyles NL-800 pedometer for seven days. Steps per day (steps/day) and time (minutes/day) spent in sedentary, light, and moderate-to-vigorous physical activity (MVPA) were examined with and without the LFE filter (AG-LFE and AG-N, respectively). The AG-LFE recorded more total steps (13,723±4,983 steps/day) compared to AG-N and NL-800 (6,172±2,838 and 5,817±3,037 steps/day, respectively; p\u3c0.001). Compared to the AG-N, the AG-LFE estimated less time in sedentary behaviors (518.7±92.1 vs. 504.2±105.4 min/day, respectively; p\u3c0.001), and more time in light (247.7±70.4 vs. 279.1±74.7 min/day, respectively; p\u3c0.001) and MVPA (18.9±16.9 vs. 21.5±18.2 min/day, respectively; p\u3c0.001), respectively. These data suggest that steps and physical activity classifications will be affected when using the ActiGraph with and without the LFE filter. Future research should investigate the accuracy of these measures using the LFE filter

Effects of Knee Alignments and Toe Clip on Frontal Plane Knee Biomechanics in Cycling

Effects of knee alignment on the internal knee abduction moment (KAM) in walking have been widely studied. The KAM is closely associated with the development of medial knee osteoarthritis. Despite the importance of knee alignment, no studies have ex- plored its effects on knee frontal plane biomechanics during sta- tionary cycling. The purpose of this study was to examine the ef- fects of knee alignment and use of a toe clip on the knee frontal plane biomechanics during stationary cycling. A total of 32 par- ticipants (11 varus, 11 neutral, and 10 valgus alignment) per- formed five trials in each of six cycling conditions: pedaling at 80 rpm and 0.5 kg (40 Watts), 1.0 kg (78 Watts), and 1.5 kg (117 Watts) with and without a toe clip. A motion analysis system and a customized instrumented pedal were used to collect 3D kine- matic and kinetic data. A 3 × 2 × 3 (group × toe clip × workload) mixed design ANOVA was used for statistical analysis (p \u3c 0.05). There were two different knee frontal plane loading patterns, in- ternal abduction and adduction moment, which were affected by knee alignment type. The knee adduction angle was 12.2° greater in the varus group compared to the valgus group (p = 0.001), yet no difference was found for KAM among groups. Wearing a toe clip increased the knee adduction angle by 0.95o (p = 0.005). The findings of this study indicate that stationary cycling may be a safe exercise prescription for people with knee malalignments. In addition, using a toe clip may not have any negative effects on knee joints during stationary cycling

Accuracy of the Cosmed K5 portable calorimeter

Purpose The purpose of this study was to assess the accuracy of the Cosmed K5 portable metabolic system dynamic mixing chamber (MC) and breath-by-breath (BxB) modes against the criterion Douglas bag (DB) method. Methods Fifteen participants (mean age±SD, 30.6±7.4 yrs) had their metabolic variables measured at rest and during cycling at 50, 100, 150, 200, and 250W. During each stage, participants were connected to the first respiratory gas collection method (randomized) for the first four minutes to reach steady state, followed by 3-min (or 5-min for DB) collection periods for the resting condition, and 2-min collection periods for all cycling intensities. Collection periods for the second and third methods were preceded by a washout of 1–3 min. Repeated measures ANOVAs were used to compare metabolic variables measured by each method, for seated rest and each cycling work rate. Results For ventilation (VE) and oxygen uptake (VO2), the K5 MC and BxB modes were within 2.1 l/min (VE) and 0.08 l/min (VO2) of the DB (p≥0.05). Compared to DB values, carbon dioxide production (VCO2) was significantly underestimated by the K5 BxB mode at work rates ≥150W by 0.12–0.31 l/min (p\u3c0.05). K5 MC and BxB respiratory exchange ratio values were significantly lower than DB at cycling work rates ≥100W by 0.03–0.08 (p\u3c0.05). Conclusion Compared to the DB method, the K5 MC and BxB modes are acceptable for measuring VE and VO2 across a wide range of cycling intensities. Both K5 modes provided comparable values to each other

Effects of Knee Alignments and Toe Clip on Frontal Plane Knee Biomechanics in Cycling

Effects of knee alignment on the internal knee abduction moment (KAM) in walking have been widely studied. The KAM is closely associated with the development of medial knee osteoarthritis. Despite the importance of knee alignment, no studies have explored its effects on knee frontal plane biomechanics during stationary cycling. The purpose of this study was to examine the effects of knee alignment and use of a toe clip on the knee frontal plane biomechanics during stationary cycling. A total of 32 participants (11 varus, 11 neutral, and 10 valgus alignment) performed five trials in each of six cycling conditions: pedaling at 80 rpm and 0.5 kg (40 Watts), 1.0 kg (78 Watts), and 1.5 kg (117 Watts) with and without a toe clip. A motion analysis system and a customized instrumented pedal were used to collect 3D kinematic and kinetic data. A 3 × 2 × 3 (group × toe clip × workload) mixed design ANOVA was used for statistical analysis (p \u3c 0.05). There were two different knee frontal plane loading patterns, internal abduction and adduction moment, which were affected by knee alignment type. The knee adduction angle was 12.2° greater in the varus group compared to the valgus group (p = 0.001), yet no difference was found for KAM among groups. Wearing a toe clip increased the knee adduction angle by 0.95º (p = 0.005). The findings of this study indicate that stationary cycling may be a safe exercise prescription for people with knee malalignments. In addition, using a toe clip may not have any negative effects on knee joints during stationary cycling

Associations of Bone Mineral Density with Lean Mass, Fat Mass, and Physical Activity in Young Overweight and Obese Women - A Feasibility Study

International Journal of Exercise Science 15(7): 585-598, 2022. To examine the associations between bone mineral density (BMD), body composition and habitual physical activity in women who are overweight/obese. We measured whole-body bone, and body composition (lean mass, fat mass, and total fat percent) via dual-energy x-ray absorptiometry (model General Electric Lunar whole-body scanner) in a diverse group of women (N=48, age 26.6+/-4.7 years, 63% Black) living in an urban setting. The relations between BMD with total fat percent [%]), lean mass (kg), fat mass (kg), and physical activity were examined using Pearson correlations and multiple linear regression models, adjusted for race, age, and dietary calcium. BMD was positively correlated with lean mass (r=0.43, p=0.002) and negatively correlated with total fat percentage (r=-0.31, p=0.03). Multiple linear regression models indicated BMD was positively associated with lean mass (β: 0.007, p\u3c0.001), and negatively associated with fat mass (kg) and total fat percentage (β: -0.003, p=0.03; β: -0.004, p=0.03, respectively). When stratified by race, these relations were maintained in white women but only lean mass in Black women. When stratified by age, the positive correlation between BMD and lean mass was significant in younger women (\u3c30y) only. There were no significant relationships between BMD and any physical activity measures. Our results indicate that in young women who are overweight/obese BMD is significantly associated with body composition, both lean mass and total fat percentage, but not habitual physical activity. An emphasis on lean mass accrual may be valuable for young women, particularly Black women, to improve bone health

Racial Differences in Neighborhood Perceptions and their Influences on Physical Activity among Urban Older Women

Background: Proper levels of physical activity (PA) are important to healthy aging. Little is known about racial differences in influences of neighborhood perceptions (NP) on PA and use of neighborhood resources among community-dwelling older women. Materials and methods: In 2014 and 2015, 49 white and 44 black women of age 65 and older living in Washington, DC were queried about their PA, NP, use of neighborhood resources and sociodemographic characteristics. They wore an accelerometer and a Global Positioning System device concurrently for 7 consecutive days. Data were analyzed by race. Results: Compared to Whites, Blacks had lower NP scores (71% positive vs. 77%, p = 0.01), lower mean daily step counts (mean (SD): 3256 (1918) vs. 5457 (2989), p \u3c 0.001), and lower frequencies of all exercise activities combined (19.7 (8.7) vs. 25.2 (11.8) per week, p = 0.01). For both Whites and Blacks, better NPs were associated with more frequent PA both at (p = 0.05) and away from home (p = 0.01). However, better NPs were associated with higher frequencies of exercise activities, moderate-to-high intensity activities, and utilitarian walking for Whites but not Blacks (p \u3c 0.05 for race-perception interaction terms). Conclusions: In an urban setting, older Black women were more likely than older White women to have poor NPs, less PA, and weaker or no association of positive NPs with higher levels of certain PAs. Such substantial racial differences warrant further investigation and consideration in health promotion programs

A Youth Compendium of Physical Activities: Activity Codes and Metabolic Intensities

A Youth Compendium of Physical Activities (Youth Compendium) was developed to estimate the energy costs of physical activities using data on youth only

Ecological monitoring of physical activity, emotions and daily life activities in schizophrenia: the DiAPAson study

Background: Schizophrenia spectrum disorders (SSD) compromise psychosocial functioning, including daily time use, emotional expression and physical activity (PA). Objective: We performed a cohort study aimed at investigating: (1) the differences in PA, daily activities and emotions between patients with SSD and healthy controls (HC); (2) the strength of the association between these variables and clinical features among patients with SSD. Methods: Ninety-nine patients with SSD (53 residential patients, 46 outpatients) and 111 matched HC were assessed for several clinical variables, and levels of functioning by means of standardised clinical measures. Self-reported daily activities and emotions were assessed with a smartphone application for ecological momentary assessment (EMA), and PA levels were assessed with a wearable accelerometer for 7 consecutive days.FindingsPatients with SSD, especially those living in residential facilities, spent more time being sedentary, and self-reported more sedentary and self-care activities, experiencing higher levels of negative emotions compared with HC. Moreover, higher functioning levels among patients were associated with more time spent in moderate-to-vigorous activity. Conclusions: Sedentary behaviour and negative emotions are particularly critical among patients with SSD and are associated with more impaired clinical outcomes. Clinical implications: Mobile-EMA and wearable sensors are useful for monitoring the daily life of patients with SSD and the level of PA. This population needs to be targeted with specific rehabilitative programmes aimed at improving their commitment to structured daily activities

Core components of a rehabilitation program in pediatric cardiac disease

There is increasing effort in both the inpatient and outpatient setting to improve care, function, and quality of life for children with congenital heart disease, and to decrease complications. As the mortality rates of surgical procedures for congenital heart disease decrease, improvement in perioperative morbidity and quality of life have become key metrics of quality of care. Quality of life and function in patients with congenital heart disease can be affected by multiple factors: the underlying heart condition, cardiac surgery, complications, and medical treatment. Some of the functional areas affected are motor abilities, exercise capacity, feeding, speech, cognition, and psychosocial adjustment. Rehabilitation interventions aim to enhance and restore functional ability and quality of life for those with physical impairments or disabilities. Interventions such as exercise training have been extensively evaluated in adults with acquired heart disease, and rehabilitation interventions for pediatric patients with congenital heart disease have similar potential to improve perioperative morbidity and quality of life. However, literature regarding the pediatric population is limited. We have gathered a multidisciplinary team of experts from major institutions to create evidence- and practice-based guidelines for pediatric cardiac rehabilitation programs in both inpatient and outpatient settings. To improve the quality of life of pediatric patients with congenital heart disease, we propose the use of individualized multidisciplinary rehabilitation programs that include: medical management; neuropsychology; nursing care; rehabilitation equipment; physical, occupational, speech, and feeding therapies; and exercise training